def _cast(g):
cast_result_name = g.generate_name('cast_result')
nodes = [
onnx.helper.make_node("Cast", [g.transients[0].name],
[cast_result_name],
to=to),
]
return g._replace(
nodes=graph.extend(g.nodes, nodes),
transients=[
onnx.helper.make_tensor_value_info(cast_result_name, to, []),
],
)
def _less_or_equal(g):
less_or_equal_result_name = g.generate_name('less_or_equal_result')
nodes = [
onnx.helper.make_node("LessOrEqual",
[g.transients[0].name, g.transients[1].name],
[less_or_equal_result_name]),
]
return g._replace(
nodes=graph.extend(g.nodes, nodes),
transients=[
onnx.helper.make_tensor_value_info(less_or_equal_result_name,
onnx.TensorProto.BOOL, []),
],
)
def _identity(g):
identity_name = g.generate_name(name)
nodes = [
onnx.helper.make_node("Identity", [g.transients[0].name],
[identity_name]),
]
return g._replace(
nodes=graph.extend(g.nodes, nodes),
transients=[
onnx.helper.make_tensor_value_info(identity_name,
onnx.TensorProto.UNDEFINED,
[]),
],
)
def _expand(g):
expand_result_name = g.generate_name('expand_result')
nodes = [
onnx.helper.make_node("Expand",
[g.transients[0].name, g.transients[1].name],
[expand_result_name]),
]
return g._replace(
nodes=graph.extend(g.nodes, nodes),
transients=[
onnx.helper.make_tensor_value_info(expand_result_name,
onnx.TensorProto.UNDEFINED,
[]),
],
)
def _add(g):
add_result_name = g.generate_name('add_result')
nodes = [
onnx.helper.make_node("Add",
[g.transients[0].name, g.transients[1].name],
[add_result_name]),
]
return g._replace(
nodes=graph.extend(g.nodes, nodes),
transients=[
onnx.helper.make_tensor_value_info(
add_result_name,
g.transients[0].type.tensor_type.elem_type, []),
],
)
def _flatten(g):
flatten_result_name = g.generate_name('flatten_result')
nodes = [
onnx.helper.make_node("Flatten", [g.transients[0].name],
[flatten_result_name],
axis=axis),
]
return g._replace(
nodes=graph.extend(g.nodes, nodes),
transients=[
onnx.helper.make_tensor_value_info(flatten_result_name,
onnx.TensorProto.UNDEFINED,
[]),
],
)
def _argmax(g):
argmax_result_name = g.generate_name('argmax_result')
nodes = [
onnx.helper.make_node("ArgMax", [g.transients[0].name],
[argmax_result_name],
axis=axis,
keepdims=keepdims,
select_last_index=select_last_index),
]
return g._replace(
nodes=graph.extend(g.nodes, nodes),
transients=[
onnx.helper.make_tensor_value_info(argmax_result_name,
onnx.TensorProto.INT64, []),
],
)
def _concat(g):
concat_result_name = g.generate_name('concat_result')
sources = [t.name for t in g.transients]
nodes = [
onnx.helper.make_node("Concat",
sources, [concat_result_name],
axis=axis),
]
return g._replace(
nodes=graph.extend(g.nodes, nodes),
transients=[
onnx.helper.make_tensor_value_info(concat_result_name,
onnx.TensorProto.UNDEFINED,
[]),
],
)
def _reshape(g):
reshape_result_name = g.generate_name('reshape_result')
nodes = [
onnx.helper.make_node(
"Reshape",
[g.transients[0].name, g.transients[1].name],
[reshape_result_name],
),
]
return g._replace(
nodes=graph.extend(g.nodes, nodes),
transients=[
onnx.helper.make_tensor_value_info(reshape_result_name,
onnx.TensorProto.UNDEFINED,
[]),
],
)
def _softmax(g):
softmax_result_name = g.generate_name('softmax_result')
nodes = [
onnx.helper.make_node(
"Softmax",
[g.transients[0].name],
[softmax_result_name],
axis=axis,
),
]
return g._replace(
nodes=graph.extend(g.nodes, nodes),
transients=[
onnx.helper.make_tensor_value_info(softmax_result_name,
onnx.TensorProto.UNDEFINED,
[]),
],
)
def _gather_elements(g):
gather_elements_result_name = g.generate_name('gather_elements_result')
nodes = [
onnx.helper.make_node(
"GatherElements",
[g.transients[0].name, g.transients[1].name],
[gather_elements_result_name],
axis=axis,
),
]
return g._replace(
nodes=graph.extend(g.nodes, nodes),
transients=[
onnx.helper.make_tensor_value_info(gather_elements_result_name,
onnx.TensorProto.UNDEFINED,
[]),
],
)
def _category_mapper(g):
category_mapper_result_name = g.generate_name('category_mapper_result')
nodes = [
onnx.helper.make_node(
"CategoryMapper",
[g.transients[0].name], [category_mapper_result_name],
cats_int64s=cats_int64s,
cats_strings=cats_strings,
default_int64=default_int64,
default_string=default_string,
domain='ai.onnx.ml',
),
]
return g._replace(
nodes=graph.extend(g.nodes, nodes),
transients=[
onnx.helper.make_tensor_value_info(category_mapper_result_name, onnx.TensorProto.UNDEFINED, []),
],
)
def _reduce_sum(g):
reduce_sum_result_name = g.generate_name('reduce_sum_result')
nodes = [
onnx.helper.make_node(
"ReduceSum",
[g.transients[0].name, g.transients[1].name],
[reduce_sum_result_name],
keepdims=keepdims,
noop_with_empty_axes=noop_with_empty_axes,
),
]
return g._replace(
nodes=graph.extend(g.nodes, nodes),
transients=[
onnx.helper.make_tensor_value_info(reduce_sum_result_name,
onnx.TensorProto.UNDEFINED,
[]),
],
)